1 Dataset

Code

datatable(a_names, filter = "top")

2 Summarizing and Visualizing

1. Summary Table

Code

a_names <- a_names |> 
  rename(Sex = Gender)
allison_names <- a_names |> 
  filter(Name == "Allison") |> 
  group_by(State, Sex) |> 
  summarize(Count) |> 
  group_by(State, Sex) |> 
  mutate(Total = sum(Count)) |> 
  distinct(Total, Sex) |> 
  pivot_wider(names_from = Sex, values_from = Total) |> 
  replace_na(list(M = 0, `F` = 0)) # https://tidyr.tidyverse.org/reference/replace_na.html

`summarise()` has grouped output by 'State', 'Sex'. You can override using the
`.groups` argument.

Code

allison_names |> 
  kable(
    col.names = c("State", "Number of Females Named Allison", "Number of Males Named Allison"),
    caption = "Summarizing Allison Names By State and Sex"
  ) |> 
  kable_minimal() |> 
  kable_styling(bootstrap_options = "striped",
                position = "left", full_width = F,
                font_size = 15)

Summarizing Allison Names By State and Sex
State	Number of Females Named Allison	Number of Males Named Allison
AK	232	0
AL	1535	0
AR	1198	0
AZ	1880	0
CA	12413	0
CO	1594	0
CT	1099	0
DC	321	0
DE	294	0
FL	4455	0
GA	3257	0
HI	183	0
IA	1477	0
ID	451	0
IL	5110	0
IN	3067	0
KS	1283	0
KY	1905	20
LA	1209	0
MA	2218	0
MD	2229	0
ME	340	0
MI	4014	0
MN	2374	0
MO	2882	0
MS	817	0
MT	226	0
NC	3435	0
ND	285	0
NE	807	0
NH	412	0
NJ	3052	0
NM	399	0
NV	729	0
NY	5747	0
OH	5487	0
OK	1421	0
OR	1186	0
PA	4307	0
RI	306	0
SC	1228	0
SD	376	0
TN	2488	0
TX	10192	0
UT	1125	0
VA	3220	0
VT	135	0
WA	1956	0
WI	2367	0
WV	813	0
WY	142	0

2. Filtering for only Female

Code

allison_names |> 
  select(State, `F`) |> 
  kable(
    col.names = c("State", "Number of Females with Allison Names")
  )

State	Number of Females with Allison Names
AK	232
AL	1535
AR	1198
AZ	1880
CA	12413
CO	1594
CT	1099
DC	321
DE	294
FL	4455
GA	3257
HI	183
IA	1477
ID	451
IL	5110
IN	3067
KS	1283
KY	1905
LA	1209
MA	2218
MD	2229
ME	340
MI	4014
MN	2374
MO	2882
MS	817
MT	226
NC	3435
ND	285
NE	807
NH	412
NJ	3052
NM	399
NV	729
NY	5747
OH	5487
OK	1421
OR	1186
PA	4307
RI	306
SC	1228
SD	376
TN	2488
TX	10192
UT	1125
VA	3220
VT	135
WA	1956
WI	2367
WV	813
WY	142

Code

  # Select was used since there is one state per row

3. Time Series Plot

Code

allison_years <- a_names |> 
  filter(Name == "Allison") |> 
  group_by(Year) |>
  mutate(Total = sum(Count)) |> 
  summarize(Total) |> 
  distinct(Year, .keep_all = TRUE)

`summarise()` has grouped output by 'Year'. You can override using the
`.groups` argument.

Code

allison_years |> 
  ggplot(mapping = aes(x = Year, y = Total)) +
    geom_line(size = 1.25, color = "Blue") +
    theme(axis.title.y = element_blank(),
         plot.title.position = "plot") +
    labs(title = "Popularity of Allison Names over the Years")

3 Modeling the Number of Allisons

4. Creating a Linear Model

Code

allison_years |> 
  lm(Total ~ Year, data = _)


Call:
lm(formula = Total ~ Year, data = allison_years)

Coefficients:
(Intercept)         Year  
   209815.1       -101.6

5. Visualizing Regression Model

Code

allison_years |> 
  ggplot(mapping = aes(x = Year, y = Total)) +
  geom_point() +
  stat_smooth(method = "lm") +
  theme(axis.title.y = element_blank(),
         plot.title.position = "plot") +
    labs(title = "Popularity of Allison Names over the Years")

`geom_smooth()` using formula 'y ~ x'

6. Regression Equation

Predicted Popularity = 209815.1 - 101.6(Year)

7. Residual Plot

Code

allison_years |> 
  lm(Total ~ Year, data = _) |> 
  broom::augment() |> 
  ggplot(mapping = aes(y = .resid, x = .fitted)) +
  geom_point()

In the residual plot I notice two peaks where the popularity of the name “Allison” is more than expected.

8. Conclusion

Your name is still cool, but unfortunately it’s not as cool as it used to be.

4 Spelling by State

9. Popularity of the Three Names

Code

a_names |> 
  filter(Name %in% c("Allen", "Alan", "Allan"), Sex == "M") |> 
  group_by(Year, Name) |>
  mutate(Total = sum(Count)) |> 
  summarize(Total) |> 
  distinct(Year, .keep_all = TRUE) |> 
  ggplot(mapping = aes(x = Year, y = Total, color = Name)) + 
    geom_line(size = 1.25) +
    labs(title = "Popularity of Names by Year") +
    theme(axis.title.y = element_blank(),
          plot.title.position = "plot")

`summarise()` has grouped output by 'Year', 'Name'. You can override using the
`.groups` argument.

10. “Allan” Spelling in Year 2000

Code

allan_names <- a_names |> 
  filter(Name %in% c("Allen", "Alan", "Allan"), Sex == "M",
         Year == 2000, State %in% c("PA", "CA")) |> 
  group_by(Year, Name) |> 
  ungroup() |> 
  select(Name, State, Count) |> 
  pivot_wider(names_from = Name, values_from = Count)
allan_names |> 
  kable(
    col.names = c("State", "Number of Alan Names", "Number of Allen Names", "Number of Allan Names")
  )

State	Number of Alan Names	Number of Allen Names	Number of Allan Names
CA	579	176	131
PA	51	56	12

11. Converting to Percentages

Code

allan_names |> 
  group_by(State) |> 
  mutate(total = sum(Alan, Allen, Allan),
         Alan = Alan/total,
         Allen = Allen/total,
         Allan = Allan/total) |> 
  select(State, Alan, Allen, Allan) |> 
  kable(
    col.names = c("State", "Percentage of Alan Names", "Percentage of Allen Names", "Percentage of Allan Names" )
    )

State	Percentage of Alan Names	Percentage of Allen Names	Percentage of Allan Names
CA	0.6534989	0.1986456	0.1478555
PA	0.4285714	0.4705882	0.1008403